temp_fangping.py

import pandas as pd
import numpy as np
from DLM_emb_model import MolEmbDLM
from transformers import AutoTokenizer
import torch
import selfies as sf

MODEL_DIR = "Kiria-Nozan/ApexOracle"

# Load model and tokenizer
tokenizer = AutoTokenizer.from_pretrained(MODEL_DIR)
model = MolEmbDLM.from_pretrained(MODEL_DIR)
model.eval()

device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
model = model.to(device)

# Load CSV data
df = pd.read_csv("temp_data/polymers_lit_scraped.csv")

# Extract all unique monomer SMILES
monomer_columns = ["monomer A", "monomer B", "monomer C", "monomer D", "monomer E", "monomer F"]
all_monomers = set()

for col in monomer_columns:
    if col in df.columns:
        monomers = df[col].dropna().unique()
        all_monomers.update(monomers)

print(f"Total unique monomers: {len(all_monomers)}")

# Convert SMILES to SELFIES and prepare for embedding
monomer_selfies = {}
valid_monomers = []

for smiles in all_monomers:
    try:
        selfies = sf.encoder(smiles)
        monomer_selfies[smiles] = selfies
        valid_monomers.append((smiles, selfies))
    except Exception as e:
        print(f"Error converting {smiles} to SELFIES: {e}")

print(f"Valid monomers for embedding: {len(valid_monomers)}")

# Generate embeddings for all monomers
monomer_embeddings = {}

for smiles, selfies in valid_monomers:
    # Prepare input similar to example.py
    batch = tokenizer(
        selfies.replace('][', '] ['),
        padding="max_length",
        max_length=1024,
        truncation=True,
        return_tensors="pt",
    )
    
    batch = {k: v.to(device) for k, v in batch.items()}
    
    with torch.no_grad():
        embeddings = model(
            input_ids=batch["input_ids"],
            attention_mask=batch["attention_mask"]+1-batch["attention_mask"],
        )
    
    # Store the embedding (average pooling over sequence length)
    monomer_embeddings[smiles] = embeddings[0][0].cpu().numpy()

print(f"Generated embeddings for {len(monomer_embeddings)} monomers")
print(f"Embedding shape: {list(monomer_embeddings.values())[0].shape}")

# Check for identical embeddings
print("\nChecking for identical embeddings...")
embedding_list = list(monomer_embeddings.items())
identical_pairs = []

for i in range(len(embedding_list)):
    for j in range(i + 1, len(embedding_list)):
        smiles1, emb1 = embedding_list[i]
        smiles2, emb2 = embedding_list[j]
        
        # Check if embeddings are identical (with small tolerance for floating point precision)
        if np.allclose(emb1, emb2, rtol=1e-09, atol=1e-09):
            identical_pairs.append((smiles1, smiles2))

if identical_pairs:
    print(f"Found {len(identical_pairs)} pairs of identical embeddings:")
    for smiles1, smiles2 in identical_pairs:
        print(f"  {smiles1} <-> {smiles2}")
    
    # Analyze the identical groups
    print("\nAnalyzing identical embedding groups...")
    
    # Create groups of molecules with identical embeddings
    identical_groups = {}
    processed = set()
    
    for smiles1, smiles2 in identical_pairs:
        if smiles1 not in processed and smiles2 not in processed:
            # Find all molecules identical to smiles1
            group = {smiles1, smiles2}
            for other_smiles1, other_smiles2 in identical_pairs:
                if other_smiles1 in group:
                    group.add(other_smiles2)
                elif other_smiles2 in group:
                    group.add(other_smiles1)
            
            group_key = frozenset(group)
            if group_key not in identical_groups:
                identical_groups[group_key] = group
                processed.update(group)
    
    print(f"Found {len(identical_groups)} groups of molecules with identical embeddings:")
    for i, group in enumerate(identical_groups.values(), 1):
        print(f"\nGroup {i} ({len(group)} molecules):")
        for smiles in sorted(group):
            selfies_repr = monomer_selfies.get(smiles, "N/A")
            print(f"  SMILES: {smiles}")
            print(f"  SELFIES: {selfies_repr}")
            print()
else:
    print("No identical embeddings found.")

# Save results
np.save("temp_data/monomer_embeddings.npy", monomer_embeddings)
print("Embeddings saved to monomer_embeddings.npy")